Recently, there has been flurry of work aimed at making objective evaluation methods non-intrusive by applying data-driven techniques. The approach of these data-driven methods is often to pose intrusive speech quality assessment as a regression problem where the goal is to map directly from the audio to the evaluation score, eliminating the requirement of a reference signal. Quality-Net [1]} uses a bidirectional long short-term memory (BLSTM) based network to predict both frame-level and utterance-level PESQ scores. Similarly, STOI-Net [2]} predicts the STOI by using a CNN-BLSTM network. Zhang et al. [3]} implement a multi-task framework and predict mean opinion score (MOS), PESQ, the extended short-time objective intelligibility (eSTOI) and signal to distortion ratio (SDR) simultaneously with a pyramid BLSTM network.
